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This project performed sentimental analysis based on opinion words (like good, bad, beautiful, wrong, best, awesome, etc) of selected opinion target ( like product name for amazon product reviews).
The Amaon Fine Foods Review dataset consists of reviews of fine foods from Amazon. There are approximate 500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. The Aim of this case study was to predict the polarity of the reviews ie. positive/negative. I have applied various Machine Le…
This project uses Machine Learning, Natural Language Processing (NLP), and Web Scraping in order to get real customer reviews for any product on Amazon and perform sentiment analysis that predicts whether the reviews are positive or negative.
An AI-powered system for automated academic peer review. Upload a PDF, and the assistant analyzes novelty, plagiarism, factual accuracy, claim mapping, and citation quality (via GROBID). Includes an optional Deep Search mode to fetch and index new papers for comparison
Sentique is a full‑stack feedback analytics platform that ingests user reviews from App Store, Google Play, Trustpilot, Reddit and Twitter/X, processes them with a fine‑tuned BERT model into 16 categories and sentiment labels, and provides comprehensive analytics and insights.
An AI assistant that digs through Google Play and Apple App Store reviews, compares up to five apps side-by-side, and tells you what actually matters. It tells what users genuinely love, what frustrates them, and what they keep asking for—so PMs can move from scattered feedback to clear, actionable product insights.
AI-powered sentiment analysis tool for evaluating processes and tracking feedback. Extract positive insights from reviews, customer feedback, and text data with real-time analysis and visualization.
This project analyzes 1,500 customer reviews from Booking.com for La Veranda Hotel (Larnaca, Cyprus) using Natural Language Processing (NLP). It performs sentiment scoring, topic modeling (LDA), and geographic sentiment analysis to uncover actionable insights that can improve hospitality operations and marketing strategy. Built using Orange.